Genomics and Proteomics Engineering in Medicine and Biology

Current applications and recent advances in genomics and proteomics Genomics and Proteomics Engineering in Medicine and Biology presents a well-rounded, interdisciplinary discussion of a topic that is at the cutting edge of both molecular biology and bioengineering. Compiling contributions by established experts, this book highlights up-to-date applications of biomedical informatics, as well as advancements in genomics-proteomics areas. Structures and algorithms are used to analyze genomic data and develop computational solutions for pathological understanding. Topics discussed include: Qualitative knowledge models Interpreting micro-array data Gene regulation bioinformatics Methods to analyze micro-array Cancer behavior and radiation therapy Error-control codes and the genome Complex life science multi-database queries Computational protein analysis Tumor and tumor suppressor proteins interactions

Preface xi Contributors xiii 1. Qualitative Knowledge Models in Functional Genomics and Proteomics 1 Mor Peleg, Irene S. Gabashvili, and Russ B. Altman 1.1. Introduction 1 1.2. Methods and Tools 3 1.3. Modeling Approach and Results 6 1.4. Discussion 19 1.5. Conclusion 20 References 21 2. Interpreting Microarray Data and Related Applications Using Nonlinear System Identification 25 Michael Korenberg 2.1. Introduction 25 2.2. Background 25 2.3. Parallel Cascade Identification 30 2.4. Constructing Class Predictors 34 2.5. Prediction Based on Gene Expression Profiling 35 2.6. Comparing Different Predictors Over the Same Data Set 46 2.7. Concluding Remarks 48 References 49 3. Gene Regulation Bioinformatics of Microarray Data 55 Gert Thijs, Frank De Smet, Yves Moreau, Kathleen Marchal, and Bart De Moor 3.1. Introduction 55 3.2. Introduction to Transcriptional Regulation 57 3.3. Measuring Gene Expression Profiles 59 3.4. Preprocessing of Data 61 3.5. Clustering of Gene Expression Profiles 63 3.6. Cluster Validation 70 3.7. Searching for Common Binding Sites of Coregulated Genes 76 3.8. Inclusive: Online Integrated Analysis of Microarray Data 87 3.9. Further Integrative Steps 89 3.10. Conclusion 90 References 91 4. Robust Methods for Microarray Analysis 99 George S. Davidson, Shawn Martin, Kevin W. Boyack, Brian N. Wylie, Juanita Martinez, Anthony Aragon, Margaret Werner-Washburne, Mónica Mosquera-Caro, and Cheryl Willman 4.1. Introduction 99 4.2. Microarray Experiments and Analysis Methods 100 4.3. Unsupervised Methods 103 4.4. Supervised Methods 117 4.5. Conclusion 127 References 128 5. In Silico Radiation Oncology: A Platform for Understanding Cancer Behavior and Optimizing Radiation Therapy Treatment 131 G. Stamatakos, D. Dionysiou, and N. Uzunoglu 5.1. Philosophiae Tumoralis Principia Algorithmica: Algorithmic Principles of Simulating Cancer on Computer 131 5.2. Brief Literature Review 133 5.3. Paradigm of Four-Dimensional Simulation of Tumor Growth and Response to Radiation Therapy In Vivo 135 5.4. Discussion 148 5.5. Future Trends 150 References 150 6. Genomewide Motif Identification Using a Dictionary Model 157 Chiara Sabatti and Kenneth Lange 6.1. Introduction 157 6.2. Unified Model 160 6.3. Algorithms for Likelihood Evaluation 164 6.4. Parameter Estimation via Minorization–Maximization Algorithm 167 6.5. Examples 170 6.6. Discussion and Conclusion 171 References 172 7. Error Control Codes and the Genome 173 Elebeoba E. May 7.1. Error Control and Communication: A Review 173 7.2. Central Dogma as Communication System 180 7.3. Reverse Engineering the Genetic Error Control System 184 7.4. Applications of Biological Coding Theory 203 References 205 8. Complex Life Science Multidatabase Queries 209 Zina Ben Miled, Nianhua Li, Yue He, Malika Mahoui, and Omran Bukhres 8.1. Introduction 209 8.2. Architecture 212 8.3. Query Execution Plans 214 8.4. Related Work 219 8.5. Future Trends 222 References 223 9. Computational Analysis of Proteins 227 Dimitrios I. Fotiadis, Yorgos Goletsis, Christos Lampros, and Costas Papaloukas 9.1. Introduction: Definitions 227 9.2. Databases 229 9.3. Sequence Motifs and Domains 232 9.4. Sequence Alignment 235 9.5. Modeling 241 9.6. Classification and Prediction 242 9.7. Natural Language Processing 248 9.8. Future Trends 252 References 252 10. Computational Analysis of Interactions Between Tumor and Tumor Suppressor Proteins 257 E. Pirogova, M. Akay, and I. Cosic 10.1. Introduction 257 10.2. Methodology: Resonant Recognition Model 261 10.3. Results and Discussions 265 10.4. Conclusion 284 References 285 Index 289 About the Editor 299